Create distinct AI agents representing key executives (e.g., CEO, CMO, CSO). By posing strategic questions to each, you can simulate how different departments might react, identify potential misalignments in priorities, and refine proposals before presenting them to real stakeholders.

Don't think of AI as replacing roles. Instead, envision a new organizational structure where every human employee manages a team of their own specialized AI agents. This model enhances individual capabilities without eliminating the human team, making everyone more effective.

To build a useful multi-agent AI system, model the agents after your existing human team. Create specialized agents for distinct roles like 'approvals,' 'document drafting,' or 'administration' to replicate and automate a proven workflow, rather than designing a monolithic, abstract AI.

True Agentic AI isn't a single, all-powerful bot. It's an orchestrated system of multiple, specialized agents, each performing a single task (e.g., qualifying, booking, analyzing). This 'division of labor,' mirroring software engineering principles, creates a more robust, scalable, and manageable automation pipeline.

Building a single, all-purpose AI is like hiring one person for every company role. To maximize accuracy and creativity, build multiple custom GPTs, each trained for a specific function like copywriting or operations, and have them collaborate.

Don't view AI tools as just software; treat them like junior team members. Apply management principles: 'hire' the right model for the job (People), define how it should work through structured prompts (Process), and give it a clear, narrow goal (Purpose). This mental model maximizes their effectiveness.

The strategy for a one-person AI-powered business isn't a single 'do-everything' agent. Instead, it's creating a team of specialized agents in different 'channels'—one for lead gen, one for blog content, one for analytics—mirroring a company's departmental structure.

Separating AI agents into distinct roles (e.g., a technical expert and a customer-facing communicator) mirrors real-world team specializations. This allows for tailored configurations, like different 'temperature' settings for creativity versus accuracy, improving overall performance and preventing role confusion.

Instead of relying on a single, all-purpose coding agent, the most effective workflow involves using different agents for their specific strengths. For example, using the 'Friday' agent for UI tasks, 'Charlie' for code reviews, and 'Claude Code' for research and backend logic.